Rashmi Shrivastava ¹, Manju Pandey ²

Affiliations
1 Bilaspur University, Bilaspur - 495001, Chhattisgarh, IN
2 National Institute of Technology, Raipur - 492001, Chhattisgarh, IN

Abstract

Objective: To provide automatically analyzing and detecting human activities to provide better support in healthcare sector, security purpose etc. Method: We have used UT Kinect-Action 3D dataset containing position of 20 body joint captured by Kinect sensor. We selected two set of joints J1 and J2; after that we have formed some rules for activity classification then we have applied SVM classifier, KNN classifier using Euclidean distance and KNN classifier using minkowski distance for activity classification. Findings: When we have used joint set J1 we got 97.8% accuracy with SVM classifier, 98.8% accuracy with KNN classifier using Euclidean distance, and 98.9% accuracy with KNN classifier using minkowski distance and for joint set J2 we got 97.7% accuracy with SVM classifier, 98.6% accuracy with KNN classifier using Euclidean distance, and 98.7% accuracy with KNN classifier using minkowski distance. Application/Improvement: we have classified four activities hand waving, standing, sitting and picking. In future more activities can also be included in this study. IOT along with this activity recognition method can be used to reduce overheads.

Keywords

Activity Recognition, IOT, Joint Set, Kinect, Skeleton.

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Akshay Hinduja ¹, Manju Pandey ¹

Affiliations
1 National Institute of Technology, Raipur - 492001, Chhattisgarh, IN

Abstract

Background/Objectives: This paper proposes a recommender system for life insurance. Insurance is a way of managing risks and has been used as financial instrument for a long time. The remarkable increase in competition within the insurance sector of the India has resulted in an overwhelming number of insurance products being available in the market. Methods: Utility theory is applied to recommend most suitable policy to users. Grey Relation Analysis (GRA) is utilized in intuitionistic fuzzy environment to determine users’ preference over alternatives. Results/Findings: The proposed recommender system has been tested with approximately 600 potential customers of different region of Chhattisgarh (INDIA). The accuracy of the recommender system is 92.6%. Also, our recommendation system has been tested with different parameters by domain experts of different levels (Branch Manager, Insurance Advisor, Development officers). They also found the results significantly accurate. Improvement: Most existing recommender system are based on collaborative filtering technique or content based system, which mainly focuses on finding relations between products and between customers through machine learning techniques. They recommend products without concerning the users’ personalized requirement. Our recommender system takes users’ current need in account and recommends most suitable policy to them. Application: The proposed recommendation technique has worked efficiently with the life insurance products and it can also be successfully applied on the products with specific preference like medical insurance, personal vehicles, and electronic home appliances.

Keywords

Grey Relational Analysis, Intuitionistic Fuzzy Sets, Life Insurance, Multi Criteria Recommender System, Utility based System

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